Research Article
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Year 2020, Volume: 25 Issue: 2, 216 - 226, 07.12.2020
https://doi.org/10.17557/tjfc.834357

Abstract

References

  • Akter, A.,M. Jamil Hassan, M. Umma Kulsum, M.R. Islam and K.Hossain. 2014. AMMI biplot analysis for stability of grain yield in hybrid rice (Oryza sativa L.). J Rice Res. 2(2): 126.
  • Ali, M.Y., S.R. Waddington, J.Timsina, D.P. Hodson and J.Dixon. 2009. Maize-rice cropping systems in Bangladesh: status and research needs. Journal of Agricultural Science and Technology 3(6): 35-53.
  • Ariyo, O.J. and M.A. Ayo-Vaughan. 2000. Analysis of genotype x environment interaction of okra (Abelmoschus esculentus (L) Moench. Journal of Genetics and Breeding 54: 33-40.
  • Badu-Apraku, B., F.J. Abamu, A. Menkir, M.A.B. Fakorade and K. Obeng-Antwi. 2003. Genotype by environment interactions in the regional early maize variety trials in West and Central Africa. Maydica 48: 93– 104.
  • Blanche, S.B. and G.O. Myers. 2006. Identifying discriminating locations for cultivar selection in Louisiana. Crop Sci. 46: 946-949.
  • Choukan, R. 2011. Genotype, environment and genotype × environment interaction effects on the performance of maize (Zea mays L.) inbred lines. C. B. Journal. 1(2): 97-103.
  • Comstock, R.E. and R.H. Moll. 1963. Genotype-Environment Interactions. Symposium on Statistical Genetics and Plant Breeding. NASNRC Publication. 982:164–96.
  • Crossa, J. 1990. Statistical analysis of multilocation trials.Advances in Agronomy 44: 55-85.
  • Crossa, J., P.N. Fox, W.H. Pfeiffer, S. Rajaram and H.G. Jr. Gauch.1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theor Appl Genet. 81: 27-37.
  • Crossa, J., H.G.J. Gauch and R.W. Zobel. 1990. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Science 30:493-500.
  • Das, S., R.C. Misra and M.C. Patnaik. 2009. GxE interaction of mid-late rice genotypes in LR and AMMI model and evaluation of adaptability and yield stability. Environment and Ecology 27(2): 529-535.
  • Dehghani, H., A. Ebadi and A. Yousefi. 2006. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agron. J. 98: 388-393.
  • Dimitrios, B., G. Christos, R. Jesus and B. Eva. 2008. Separation of cotton cultivar testing sites based on representativeness and discriminating ability using GGE Biplots. Agron. J. 100: 1230-1236.
  • Duarte, J.B. and R.Vencovsky.1999.Interação genótipos xambientes: uma introdução a análise AMMI. ESALQ/USP,Ribeirão Preto, SP.
  • Ebdon, J.S. and H.G. Gauch. 2002. Additive Main Effect and Multiplicative Interaction analysis of national turfgrass performance trials: II. Cultivar recommendations. Crop Science. 42: 497-506.
  • Eberhart, S.A. and W.A. Russell. 1966. Stability parameters for comparing varieties. Crop Sci. 6: 36–40.
  • FAOSTAT 2018. FAO statistical database. FAO, Rome, Italy.
  • Fan, X.M., M.S. Kang, H. Chen, Y. Zhang, J. Tan and C. Xu. 2007. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agron. J. 99: 220-228.
  • Finlay, K.W. and G.N. Wilkinson 1963. The analysis of adaptation in a plant breeding programme. Australian J. Agric. Sci. 14: 742–54.
  • Francis, T.R. and L.W. Kannenberg. 1978. Yield stability studies in short-season maize I. A descriptive method for grouping genotypes. Canadian J. Pl. Sci. 58: 1029–1034.
  • Freeman, G.H. 1985. The analysis and interpretation of interaction. Journal of Applied Statistics. 12(1): 3-10.
  • Gabriel, K.R. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika. 58: 453-467.
  • Gauch, H.G. 1993. Matmodel version 2.0. AMMI and related analysis for two-way data matrics. Micro computer power, Ithaca, New York, USA.
  • Gauch, H.G. 2006. Statistical analysis of yield trials byAMMI and GGE. Crop Sci. 46: 1488-1500.
  • Gauch, H.G. and R.W. Zobel. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311-326.
  • Gerpacio, V.R. and P.L. Pingali. 2007. Tropical and subtropical maize in Asia: Production systems, constraints and research priorities. Mexico, D.F:CIMMYT.
  • Gulati, A., J. Dixon and J.M. Dixon. 2008. Maize in Asia: changing markets and incentives. Contributors: International Maize and Wheat Improvement Center, International Food Policy Research Institute, International Fund for Agricultural Development. Academic Foundation, pp.489.
  • Hossain, A., M.Farhad, M.A.H.S. Jahan, M.G. Mahboob, J. Timsina and J.A. Teixeira da Silva. 2018. Biplot yield analysis of heat-tolerant spring wheat genotypes (Triticum aestivum L.) in multiple growing environments. Open Agriculture 3(1): 404-413.
  • Intergovernmental Panel on Climate Change (IPCC). Climate Change 2007b: Impacts, Adaptation And Vulnerability: An Assessment Report Of The Intergovernmental Panel On Climate Change; Cambridge University Press: Cambridge, UK, 2007.
  • IPCC. 2001. Climate Change 2001: Impacts, Adaptation and Vulnerability, Summary For Policymakers and Technical Summary of the Working Group II Report, Geneva.
  • IPCC. 2007. Climate Change 2007a: Impacts, Adaptation and Vulnerability: Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In: Parry ML, Canziani OF, Palutikof JP, Van Der Linden PJ, Hanson CE (Eds) Cambridge University Press, Cambridge.
  • Kaya, Y., M. Akcura and S. Taner. 2006. GGE-biplot analysis of multi-environment yield trials in bread wheat. Turk. J. Agric. Forest. 30: 325-337.
  • Kempton, R.A. 1984. The use of biplots in interpreting variety by environment interactions. Journal of Agricultural Science 103: 123-135.
  • Kizilgeci, F., O. Albayrak and M. Yildirim. 2019. Evaluation of thirteen durum wheat (Triticium durum Desf.) genotypes suitable for multiple environments using GGE biplot analysis. Fresenius Environmental Bulletin, 28(9): 6873-6882.
  • Knight, R. 1970. The measurement and interpretation of genotype-environment interactions. Euphytica 19(2): 225–235.
  • Langridge, J. and B. Griffing. 1959. A study of high-temperature lesions in Arabidopsis thaliana. Australian J. Biol. Sci. 12: 117–35.
  • Letta, T., M.G. D’Egidio and M. Abinasa. 2008.Analysis of multi-environment yield trials in durum wheat based on GGE-biplot electronic resource. J.Food, Agric. Environ. 6(2): 217-221.
  • Lin, C.S., M.R. Binns and L.P. Lefkovitch. 1986. Stability analysis: Where do we stand? Crop Sci. 26: 894–900.
  • McLaren, C.G. and C. Chaudhary. 1994. Use of additive main effects and multiplicative interaction models to analyse multilocation rice variety trials. Paper presented at the FCSSP Conference, Puerton Princesa, Palawan, Philippines.
  • Mohammadi, R., A. Amri and Y. Ansari. 2009. BiplotAnalysis of rain-fed barley multi-environment trials in Iran. Agron. J. 101: 789-796.
  • Mohammadi, R., M. Armion, E. Zadhasan, M.M. Ahmadi and D. Sadeghzadeh Ahari. 2012. Genotype × environment interaction for grain yield of rainfed durum wheat using the GGE biplot model. Seed and plant Improv. J. 28-1(3): 503-518.
  • Mohammadi, R., R. Haghparast, A. Amri and S. Ceccarelli. 2010. Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials. Crop Past. Sci. 61: 92-101.
  • Moreno-Gonzalez, J., J. Crossa and P. L. Cornelius. 2004. Genotype × environment interaction in multi-environment trials using shrinkage factors for AMMI models. Euphytica. 137: 119-127.
  • Nasreen, M. 2008. Sustainable development and impacts of climate change: A Gender Perspective. ITN, BUET.
  • Nassir, A.L. 2013. Genotype x Environment analysis of some yield components of upland rice (Oryza sativa L.) under two ecologies in Nigeria. InternationalJournal of Plant Breeding and Genetics. 7: 105-114.
  • Ozaki, M.2016. Disaster risk financing in Bangladesh, ADB SOUTH ASIA Working Paper Series, no. 46.
  • Pacheco, R.M., J.B. Duarte, M.D.S. Assunção, J. Nunes Júnior and A.A.P Chaves. 2003. Zoneamento e adaptação produtiva de genótiposde soja de ciclo médio de maturação para Goiás. PesquisaAgropecuária Tropical 33: 23-27.
  • Perkins, J.M. and J.L. Jinks. 1968. Environmental and genotype-environmental components of variability. III. Multiple lines and crosses. Heredity 23: 339–356.
  • Peterson, C.J. and W.H. Pfeiffer, 1989. International Winter Wheat Evaluation: Relationships among Test Sites Based on Cultivar Performance. Crop Sci. 29: 276–282.
  • Peterson, C.J. 1992. Similarities of test sites based on cultivar performance in the hard red winter wheat region. Crop Sci. 32: 907–912.
  • Plaisted, R.L. and L.C. Peterson. 1959. A technique for evaluating the ability of selections to yield consistently in different locations or season. American Potato J. 36: 381-385.
  • Plaisted, R.L. 1960. A shorter method for evaluating the ability of selections to yield consistently over locations. American Potato J. 37: 166–172.
  • Prasad, K.V. and R.L.Singh. 1990. Stability analysis of yield and yield components and construction of selection indices of direct-seeded rice in frost season.Annual Review conference Proceeding. 20-23 October 1992. National Agric.Res. Inst. Caribbean Agricultural Research and Development Institute, Guyana, pp. 63-71.
  • Prasanna, B.M., A. Das and K.K. Kaimenyi. 2018. Book of Extended Summaries, 13th Asian Maize Conference and Expert Consultation on Maize for Food, Feed, Nutrition and Environmental Security. Ludhiana, India, October 8 – 10, 2018. CIMMYT, Mexico D.F.
  • R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. ISBN 3-900051-07-0, URL <http://www.R-project.org/>, (Last accessed on 11 March, 2019)
  • Samonte, S.O.P.B., L.T. Wilson, A.M. Mc Clung andJ.C. Medley. 2005. Targeting cultivars onto ricegrowing environments using AMMI and SREG GGEbiplot analysis. Crop Sci. 45: 2414-2424.
  • Rahman, S., Md. Touhiduzzaman and I. Hasan. 2017. Coastal livelihood vulnerability to climate change: a case study of char montaz in Patuakhali District of Bangladesh. American Journal of Modern Energy 3(4): 58-64.
  • Shinde, G.C., M.T. Bhingarde and S.S. Mehetre. 2002. AMMI analysis for stability of grain yield of pearl millet (Pennisetum typhoides L.) hybrids. InternationalJournal of Genetics 62: 215-217.
  • Shiferaw, B., B. Prasanna, J. Hellin and M. Banziger. 2011. Crops that feed the world. 6. Past successes and future challenges to the role played by maize in global food security. Food Security 3: 307–327.
  • Shiri, M. 2013. Grain yield stability analysis of maize (Zea mays L.) hybrids in different drought stress conditions using GGE biplot analysis. Crop Breeding Journal 3(2):107-112.
  • Shukla, G.K. 1972. Some statistical aspects of partitioning genotype environment components of variability. Heredity 29: 237–245.
  • Tarakanovas T. and V. Rugas. 2006. Additive main effect and multiplicativeinteraction analysis of grain yield of wheat varieties in Lithuania. Agronomy Research 4: 91-98.
  • Taye. G., T. Getachew and G. Bejiga. 2000. AMMI adjustment for yield estimate and classifications of genotypes and environments in field pea (Pisum sativum L.). Journal of Genetics and Breeding 54: 183-191.
  • Timsina, J., M.L. Jat and K. Majumdar. 2010. Rice-maize systems of South Asia: current status, future prospects and research priorities for nutrient management. Plant and Soil. 335(1-2): 65-82.
  • Timsina, J., J. Wolf, N. Guilpart, L.G.J. Van Bussel, P. Grassini, J. Van Wart, A. Hossain, H. Rashid, S. Islam and M.K. Van Ittersum. 2018. Can Bangladesh produce enough cereals to meet future demand?. Agricultural systems 163: 36-44.
  • Wricke, G. 1962. Uber eine Methode zur Erfassung der okologischen Streubreite in Feldversuchen Z.Pflanzenzuecht, 47: 92–96.
  • Yan, W. andL.A.Hunt. 2001. Interpretation of genotype x environment interaction for winter wheat yield in Ontario. Crop Science 41: 19-25.
  • Yan, W. and I. Rajcan. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci. 42: 11-20.
  • Yan, W. and L. A. Hunt. 2002. Biplot analysis of diallel data. Crop Sci. 42: 21-30.
  • Yan, W. and M. S. Kang. 2003. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, FL, USA.
  • Yan, W. and N. A. Tinker. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Can. J. Plant Sci. 86: 623-645.
  • Yan, W., L.A. Hunt, Q. Sheng and Z. Szlavnics. 2000. Cultivar evaluation and mega-environment investigations based on the GGE biplot. Crop Sci. 40: 597-605.
  • Yan, W., P.L. Cornelius, J. Crossa and L.A. Hunt. 2001. Two types of GGE biplots for analyzing multi-environment trial data. Crop Sci. 41: 656-663.
  • Yau, S.K. 1995. Regression and AMMI analyses of genotype x environment interactions: An empirical comparison. Agronomy Journal 87: 121-126.
  • Zobel, R.W., M.J. Wright and H.G. Gauch. 1988. Statistical analysis of a yield trial. Agronomy Journal. 80: 388-393.

EVALUATING SHORT STATURE AND HIGH YIELDING MAIZE HYBRIDS IN MULTIPLE ENVIRONMENTS USING GGE BIPLOT AND AMMI MODELS

Year 2020, Volume: 25 Issue: 2, 216 - 226, 07.12.2020
https://doi.org/10.17557/tjfc.834357

Abstract

In Bangladesh, maize stands second place after rice; since it faces diverse natural calamities during its highest growing season (rabi/winter), particularly strong storm during the reproductive stage. Sometimes in some regions, this crop is completely damaged by natural disasters. Considering the burning issue, thirteen hybrids, including 10 previously selected short stature hybrids were evaluated against three local and standard checks: ‘BHM-9’, ‘981’ and ‘Sunshine’ in two consecutive years in seven locations of Bangladesh. Combined analysis over locations and seasons instigated that genotypes ‘Sunshine’, ‘981’ and ‘G10’ were the top-high yielders, while genotypes ‘G1’, ‘G2’, ‘BHM-9’ and ‘Sunshine’ were found the most stable. On the other hand, five genotypes such as ‘G3’, ‘G4’, ‘G6’, ‘G8’ and ‘G9’ had the below-average mean yield and the genotypes ‘G6’ and ‘G9’ were the most unstable. Among the seven environments, Jamalpur, Joydebpur and Dinajpur were most discriminating and Ishwardi was the least discriminating; whereas Joydebpur was more representative and Borishal was the least representative of other test environments. In the case of plant and ear height, most of the genotypes showed a lower value than all the checks, which was desirable. But among the top three high yielders, local cross-genotype, the ‘G10’ had the lowest and more stable value for both plant height and ear height. Therefore, considering the plant and ear height, grain yield, and yield stability, the genotype ‘G10’ has been recommended for release as commercial variety and has been released as new maize variety in Bangladesh with the local name of ‘BARI Hybrid Maize-16’ (BHM-16).

References

  • Akter, A.,M. Jamil Hassan, M. Umma Kulsum, M.R. Islam and K.Hossain. 2014. AMMI biplot analysis for stability of grain yield in hybrid rice (Oryza sativa L.). J Rice Res. 2(2): 126.
  • Ali, M.Y., S.R. Waddington, J.Timsina, D.P. Hodson and J.Dixon. 2009. Maize-rice cropping systems in Bangladesh: status and research needs. Journal of Agricultural Science and Technology 3(6): 35-53.
  • Ariyo, O.J. and M.A. Ayo-Vaughan. 2000. Analysis of genotype x environment interaction of okra (Abelmoschus esculentus (L) Moench. Journal of Genetics and Breeding 54: 33-40.
  • Badu-Apraku, B., F.J. Abamu, A. Menkir, M.A.B. Fakorade and K. Obeng-Antwi. 2003. Genotype by environment interactions in the regional early maize variety trials in West and Central Africa. Maydica 48: 93– 104.
  • Blanche, S.B. and G.O. Myers. 2006. Identifying discriminating locations for cultivar selection in Louisiana. Crop Sci. 46: 946-949.
  • Choukan, R. 2011. Genotype, environment and genotype × environment interaction effects on the performance of maize (Zea mays L.) inbred lines. C. B. Journal. 1(2): 97-103.
  • Comstock, R.E. and R.H. Moll. 1963. Genotype-Environment Interactions. Symposium on Statistical Genetics and Plant Breeding. NASNRC Publication. 982:164–96.
  • Crossa, J. 1990. Statistical analysis of multilocation trials.Advances in Agronomy 44: 55-85.
  • Crossa, J., P.N. Fox, W.H. Pfeiffer, S. Rajaram and H.G. Jr. Gauch.1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theor Appl Genet. 81: 27-37.
  • Crossa, J., H.G.J. Gauch and R.W. Zobel. 1990. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Science 30:493-500.
  • Das, S., R.C. Misra and M.C. Patnaik. 2009. GxE interaction of mid-late rice genotypes in LR and AMMI model and evaluation of adaptability and yield stability. Environment and Ecology 27(2): 529-535.
  • Dehghani, H., A. Ebadi and A. Yousefi. 2006. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agron. J. 98: 388-393.
  • Dimitrios, B., G. Christos, R. Jesus and B. Eva. 2008. Separation of cotton cultivar testing sites based on representativeness and discriminating ability using GGE Biplots. Agron. J. 100: 1230-1236.
  • Duarte, J.B. and R.Vencovsky.1999.Interação genótipos xambientes: uma introdução a análise AMMI. ESALQ/USP,Ribeirão Preto, SP.
  • Ebdon, J.S. and H.G. Gauch. 2002. Additive Main Effect and Multiplicative Interaction analysis of national turfgrass performance trials: II. Cultivar recommendations. Crop Science. 42: 497-506.
  • Eberhart, S.A. and W.A. Russell. 1966. Stability parameters for comparing varieties. Crop Sci. 6: 36–40.
  • FAOSTAT 2018. FAO statistical database. FAO, Rome, Italy.
  • Fan, X.M., M.S. Kang, H. Chen, Y. Zhang, J. Tan and C. Xu. 2007. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agron. J. 99: 220-228.
  • Finlay, K.W. and G.N. Wilkinson 1963. The analysis of adaptation in a plant breeding programme. Australian J. Agric. Sci. 14: 742–54.
  • Francis, T.R. and L.W. Kannenberg. 1978. Yield stability studies in short-season maize I. A descriptive method for grouping genotypes. Canadian J. Pl. Sci. 58: 1029–1034.
  • Freeman, G.H. 1985. The analysis and interpretation of interaction. Journal of Applied Statistics. 12(1): 3-10.
  • Gabriel, K.R. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika. 58: 453-467.
  • Gauch, H.G. 1993. Matmodel version 2.0. AMMI and related analysis for two-way data matrics. Micro computer power, Ithaca, New York, USA.
  • Gauch, H.G. 2006. Statistical analysis of yield trials byAMMI and GGE. Crop Sci. 46: 1488-1500.
  • Gauch, H.G. and R.W. Zobel. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311-326.
  • Gerpacio, V.R. and P.L. Pingali. 2007. Tropical and subtropical maize in Asia: Production systems, constraints and research priorities. Mexico, D.F:CIMMYT.
  • Gulati, A., J. Dixon and J.M. Dixon. 2008. Maize in Asia: changing markets and incentives. Contributors: International Maize and Wheat Improvement Center, International Food Policy Research Institute, International Fund for Agricultural Development. Academic Foundation, pp.489.
  • Hossain, A., M.Farhad, M.A.H.S. Jahan, M.G. Mahboob, J. Timsina and J.A. Teixeira da Silva. 2018. Biplot yield analysis of heat-tolerant spring wheat genotypes (Triticum aestivum L.) in multiple growing environments. Open Agriculture 3(1): 404-413.
  • Intergovernmental Panel on Climate Change (IPCC). Climate Change 2007b: Impacts, Adaptation And Vulnerability: An Assessment Report Of The Intergovernmental Panel On Climate Change; Cambridge University Press: Cambridge, UK, 2007.
  • IPCC. 2001. Climate Change 2001: Impacts, Adaptation and Vulnerability, Summary For Policymakers and Technical Summary of the Working Group II Report, Geneva.
  • IPCC. 2007. Climate Change 2007a: Impacts, Adaptation and Vulnerability: Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In: Parry ML, Canziani OF, Palutikof JP, Van Der Linden PJ, Hanson CE (Eds) Cambridge University Press, Cambridge.
  • Kaya, Y., M. Akcura and S. Taner. 2006. GGE-biplot analysis of multi-environment yield trials in bread wheat. Turk. J. Agric. Forest. 30: 325-337.
  • Kempton, R.A. 1984. The use of biplots in interpreting variety by environment interactions. Journal of Agricultural Science 103: 123-135.
  • Kizilgeci, F., O. Albayrak and M. Yildirim. 2019. Evaluation of thirteen durum wheat (Triticium durum Desf.) genotypes suitable for multiple environments using GGE biplot analysis. Fresenius Environmental Bulletin, 28(9): 6873-6882.
  • Knight, R. 1970. The measurement and interpretation of genotype-environment interactions. Euphytica 19(2): 225–235.
  • Langridge, J. and B. Griffing. 1959. A study of high-temperature lesions in Arabidopsis thaliana. Australian J. Biol. Sci. 12: 117–35.
  • Letta, T., M.G. D’Egidio and M. Abinasa. 2008.Analysis of multi-environment yield trials in durum wheat based on GGE-biplot electronic resource. J.Food, Agric. Environ. 6(2): 217-221.
  • Lin, C.S., M.R. Binns and L.P. Lefkovitch. 1986. Stability analysis: Where do we stand? Crop Sci. 26: 894–900.
  • McLaren, C.G. and C. Chaudhary. 1994. Use of additive main effects and multiplicative interaction models to analyse multilocation rice variety trials. Paper presented at the FCSSP Conference, Puerton Princesa, Palawan, Philippines.
  • Mohammadi, R., A. Amri and Y. Ansari. 2009. BiplotAnalysis of rain-fed barley multi-environment trials in Iran. Agron. J. 101: 789-796.
  • Mohammadi, R., M. Armion, E. Zadhasan, M.M. Ahmadi and D. Sadeghzadeh Ahari. 2012. Genotype × environment interaction for grain yield of rainfed durum wheat using the GGE biplot model. Seed and plant Improv. J. 28-1(3): 503-518.
  • Mohammadi, R., R. Haghparast, A. Amri and S. Ceccarelli. 2010. Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials. Crop Past. Sci. 61: 92-101.
  • Moreno-Gonzalez, J., J. Crossa and P. L. Cornelius. 2004. Genotype × environment interaction in multi-environment trials using shrinkage factors for AMMI models. Euphytica. 137: 119-127.
  • Nasreen, M. 2008. Sustainable development and impacts of climate change: A Gender Perspective. ITN, BUET.
  • Nassir, A.L. 2013. Genotype x Environment analysis of some yield components of upland rice (Oryza sativa L.) under two ecologies in Nigeria. InternationalJournal of Plant Breeding and Genetics. 7: 105-114.
  • Ozaki, M.2016. Disaster risk financing in Bangladesh, ADB SOUTH ASIA Working Paper Series, no. 46.
  • Pacheco, R.M., J.B. Duarte, M.D.S. Assunção, J. Nunes Júnior and A.A.P Chaves. 2003. Zoneamento e adaptação produtiva de genótiposde soja de ciclo médio de maturação para Goiás. PesquisaAgropecuária Tropical 33: 23-27.
  • Perkins, J.M. and J.L. Jinks. 1968. Environmental and genotype-environmental components of variability. III. Multiple lines and crosses. Heredity 23: 339–356.
  • Peterson, C.J. and W.H. Pfeiffer, 1989. International Winter Wheat Evaluation: Relationships among Test Sites Based on Cultivar Performance. Crop Sci. 29: 276–282.
  • Peterson, C.J. 1992. Similarities of test sites based on cultivar performance in the hard red winter wheat region. Crop Sci. 32: 907–912.
  • Plaisted, R.L. and L.C. Peterson. 1959. A technique for evaluating the ability of selections to yield consistently in different locations or season. American Potato J. 36: 381-385.
  • Plaisted, R.L. 1960. A shorter method for evaluating the ability of selections to yield consistently over locations. American Potato J. 37: 166–172.
  • Prasad, K.V. and R.L.Singh. 1990. Stability analysis of yield and yield components and construction of selection indices of direct-seeded rice in frost season.Annual Review conference Proceeding. 20-23 October 1992. National Agric.Res. Inst. Caribbean Agricultural Research and Development Institute, Guyana, pp. 63-71.
  • Prasanna, B.M., A. Das and K.K. Kaimenyi. 2018. Book of Extended Summaries, 13th Asian Maize Conference and Expert Consultation on Maize for Food, Feed, Nutrition and Environmental Security. Ludhiana, India, October 8 – 10, 2018. CIMMYT, Mexico D.F.
  • R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. ISBN 3-900051-07-0, URL <http://www.R-project.org/>, (Last accessed on 11 March, 2019)
  • Samonte, S.O.P.B., L.T. Wilson, A.M. Mc Clung andJ.C. Medley. 2005. Targeting cultivars onto ricegrowing environments using AMMI and SREG GGEbiplot analysis. Crop Sci. 45: 2414-2424.
  • Rahman, S., Md. Touhiduzzaman and I. Hasan. 2017. Coastal livelihood vulnerability to climate change: a case study of char montaz in Patuakhali District of Bangladesh. American Journal of Modern Energy 3(4): 58-64.
  • Shinde, G.C., M.T. Bhingarde and S.S. Mehetre. 2002. AMMI analysis for stability of grain yield of pearl millet (Pennisetum typhoides L.) hybrids. InternationalJournal of Genetics 62: 215-217.
  • Shiferaw, B., B. Prasanna, J. Hellin and M. Banziger. 2011. Crops that feed the world. 6. Past successes and future challenges to the role played by maize in global food security. Food Security 3: 307–327.
  • Shiri, M. 2013. Grain yield stability analysis of maize (Zea mays L.) hybrids in different drought stress conditions using GGE biplot analysis. Crop Breeding Journal 3(2):107-112.
  • Shukla, G.K. 1972. Some statistical aspects of partitioning genotype environment components of variability. Heredity 29: 237–245.
  • Tarakanovas T. and V. Rugas. 2006. Additive main effect and multiplicativeinteraction analysis of grain yield of wheat varieties in Lithuania. Agronomy Research 4: 91-98.
  • Taye. G., T. Getachew and G. Bejiga. 2000. AMMI adjustment for yield estimate and classifications of genotypes and environments in field pea (Pisum sativum L.). Journal of Genetics and Breeding 54: 183-191.
  • Timsina, J., M.L. Jat and K. Majumdar. 2010. Rice-maize systems of South Asia: current status, future prospects and research priorities for nutrient management. Plant and Soil. 335(1-2): 65-82.
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  • Wricke, G. 1962. Uber eine Methode zur Erfassung der okologischen Streubreite in Feldversuchen Z.Pflanzenzuecht, 47: 92–96.
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There are 75 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Asgar Ahmed This is me

Akbar Hossaın This is me

Md. Amıruzzaman This is me

Md. Ashraful Alam This is me

Muhammad Farooq This is me

Ayman El Sabagh This is me

Ferhat Kızılgecı

Publication Date December 7, 2020
Published in Issue Year 2020 Volume: 25 Issue: 2

Cite

APA Ahmed, A., Hossaın, A., Amıruzzaman, M., Alam, M. A., et al. (2020). EVALUATING SHORT STATURE AND HIGH YIELDING MAIZE HYBRIDS IN MULTIPLE ENVIRONMENTS USING GGE BIPLOT AND AMMI MODELS. Turkish Journal Of Field Crops, 25(2), 216-226. https://doi.org/10.17557/tjfc.834357
AMA Ahmed A, Hossaın A, Amıruzzaman M, Alam MA, Farooq M, El Sabagh A, Kızılgecı F. EVALUATING SHORT STATURE AND HIGH YIELDING MAIZE HYBRIDS IN MULTIPLE ENVIRONMENTS USING GGE BIPLOT AND AMMI MODELS. TJFC. December 2020;25(2):216-226. doi:10.17557/tjfc.834357
Chicago Ahmed, Asgar, Akbar Hossaın, Md. Amıruzzaman, Md. Ashraful Alam, Muhammad Farooq, Ayman El Sabagh, and Ferhat Kızılgecı. “EVALUATING SHORT STATURE AND HIGH YIELDING MAIZE HYBRIDS IN MULTIPLE ENVIRONMENTS USING GGE BIPLOT AND AMMI MODELS”. Turkish Journal Of Field Crops 25, no. 2 (December 2020): 216-26. https://doi.org/10.17557/tjfc.834357.
EndNote Ahmed A, Hossaın A, Amıruzzaman M, Alam MA, Farooq M, El Sabagh A, Kızılgecı F (December 1, 2020) EVALUATING SHORT STATURE AND HIGH YIELDING MAIZE HYBRIDS IN MULTIPLE ENVIRONMENTS USING GGE BIPLOT AND AMMI MODELS. Turkish Journal Of Field Crops 25 2 216–226.
IEEE A. Ahmed, “EVALUATING SHORT STATURE AND HIGH YIELDING MAIZE HYBRIDS IN MULTIPLE ENVIRONMENTS USING GGE BIPLOT AND AMMI MODELS”, TJFC, vol. 25, no. 2, pp. 216–226, 2020, doi: 10.17557/tjfc.834357.
ISNAD Ahmed, Asgar et al. “EVALUATING SHORT STATURE AND HIGH YIELDING MAIZE HYBRIDS IN MULTIPLE ENVIRONMENTS USING GGE BIPLOT AND AMMI MODELS”. Turkish Journal Of Field Crops 25/2 (December 2020), 216-226. https://doi.org/10.17557/tjfc.834357.
JAMA Ahmed A, Hossaın A, Amıruzzaman M, Alam MA, Farooq M, El Sabagh A, Kızılgecı F. EVALUATING SHORT STATURE AND HIGH YIELDING MAIZE HYBRIDS IN MULTIPLE ENVIRONMENTS USING GGE BIPLOT AND AMMI MODELS. TJFC. 2020;25:216–226.
MLA Ahmed, Asgar et al. “EVALUATING SHORT STATURE AND HIGH YIELDING MAIZE HYBRIDS IN MULTIPLE ENVIRONMENTS USING GGE BIPLOT AND AMMI MODELS”. Turkish Journal Of Field Crops, vol. 25, no. 2, 2020, pp. 216-2, doi:10.17557/tjfc.834357.
Vancouver Ahmed A, Hossaın A, Amıruzzaman M, Alam MA, Farooq M, El Sabagh A, Kızılgecı F. EVALUATING SHORT STATURE AND HIGH YIELDING MAIZE HYBRIDS IN MULTIPLE ENVIRONMENTS USING GGE BIPLOT AND AMMI MODELS. TJFC. 2020;25(2):216-2.

Turkish Journal of Field Crops is published by the Society of Field Crops Science and issued twice a year.
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